Factors associated with inability to access addiction treatment among people who inject drugs in Vancouver, Canada
Bibliographic record
Abstract
BACKGROUND: Addiction treatment is an effective strategy used to reduce drug-related harm. In the wake of recent developments in novel addiction treatment modalities, we conducted a longitudinal data analysis to examine factors associated with inability to access addiction treatment among a prospective cohort of persons who inject drugs (PWID). METHODS: Data were derived from two prospective cohorts of PWID in Vancouver, Canada, between December 2005 and November 2013. Using multivariate generalized estimating equations, we examined factors associated with reporting an inability to access addiction treatment. RESULTS: In total, 1142 PWID who had not accessed any addiction treatment during the six months prior to interview were eligible for this study, including 364 women (31.9 %). Overall, 188 (16.5 %) reported having sought but were ultimately unsuccessful in accessing addiction treatment at least once during the study period. In multivariate analysis, factors independently and positively associated with reporting inability to access addiction treatment included: binge drug use (Adjusted Odds Ratio [AOR] = 1.65), being a victim of violence (AOR = 1.77), homelessness (AOR = 1.99), and having ever accessed addiction treatment (AOR = 2.33); while length of time injecting was negatively and independently associated (AOR = 0.98) (all p < 0.05). CONCLUSIONS: These findings suggest that sub-populations of PWID were more likely to report experiencing difficulty accessing addiction treatment, including those who may be entrenched in severe drug addiction and vulnerable to violence. It is imperative that additional resources go into ensuring treatment options are readily available when requested for these target populations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".